C-PAII10-35 無料問題集「SAP Certified Application Associate - SAP Predictive Analytics」

（A）Or create a description file using the Analyze option. In this case, it is important that you validate the description file obtained. You can save this file for later re-use.

（B）Use an existing description file, that is, taken from your information system or saved from a previous use of Automated Analytics.

（C）A web server such as Apache Web Server or Windows Internet
Information Services (IIS).

（A）They are automatically filled.

（B）They are considered as outliers.

（C）They are assigned to a category.

（D）They are automatically ignored.

（A）The curves for the different profit types: Detected, Lift,
Normalized, and Customized. For more information on profit
types, see the related topic.

（B）Exact profit values for a point for all the displayed curves.

（C）A web server (Apache Web Server for example). See the
sections below to install Apache Web Server

（A）Otherwise, you can modify the modeling parameters in such a way that they are better suited to your dataset and your business issue, and then generate new, more powerful models.

（B）The Progress Bar displays the progression for each step of the process. It is the screen displayed by default.

（C）If the model is sufficiently powerful, you can analyze the responses that it provides in relation to your business issue apply it to new datasets

（D）The Detailed Log displays the details of each step of the process.

（A）You can also check other indicators provided in addition to KI and KR during the model generation. For example, you could view the total elapsed time required to generate the model and information on the standard error rate.

（B）Verify the Predictive Power Automated Analytics User Guides and Scenarios Modeler If the performance of the model meets your requirements, go to Step 3 - Analyzing and Understanding the Model Generated. Otherwise, go to the procedure To Generate a New Model.

（C）A web server such as Apache Web Server or Windows Internet Information Services (IIS).

（A）and the number of variables for which the application has found deviations in comparison to the dataset originally used to train the model (Number of variables showing deviation)

（B）the number of records contained in the dataset (Number of Records)

（C）the source file (Source)

（D）A web server (Apache Web Server for example). See the sections below to install Apache Web Server

（E）the name of the dataset (Dataset)

（A）A Java Runtime Environment installed on the client machine to connect to the server

（B）The performance of the model with respect to a hypothetical perfect model and a random type of model,

（C）The contribution of each of the explanatory variables with respect to the target variable,

（D）The significance of the various categories of each variable with respect to the target variable.

（A）Three lines of the first 4/5 of the initial dataset are distributed to the training subset.

（B）If the entire 4/5 of the initial dataset is not yet distributed, distribution operations begin again at step 1. Otherwise, if the entire 4/5 of the initial dataset has been distributed, distribution operations go to step

（C）The final 1/5 of the initial dataset is sent as a block of data to the testing sub-set.

（D）The name server and the server will be assigned a specific TCP port. By default, the startup scripts we use fix them, and they should probably set properly depending on the network strategy

（E）One line of the first 4/5 of the initial dataset is distributed to the validation sub-set.